Useful R functions

⌂ Contents 📁 Materials ← Vectors Factors →

Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 102 187 601 544 456 646 862 623 319 375 177 158 212 995 996 723 334  26  85 429

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 623 456  NA 995 646 601  NA 334 319 102 158 862  85 187 429  NA 375  26 544 177 212 723 996

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 1 5 5 5 2 2 5 2 5 4

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y"
[26] "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y"
[26] "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "g" "u" "q" "w" "k" "C" "O" "F" "U" "Q"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 14 15

which( manyNumbersWithNA > 900 )

[1]  4 23

which( is.na( manyNumbersWithNA ) )

[1]  3  7 16

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 995 996

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 995 996

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 995 996

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "C" "O" "F" "U" "Q"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "g" "u" "q" "w" "k"

manyNumbers %in% 300:600

 [1] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[18] FALSE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1]  4  5  9 10 17 20

sum( manyNumbers %in% 300:600 )

[1] 6

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "large" "small" NA      "large" "large" "large" NA      "small" "small" "small" "small" "large"
[13] "small" "small" "small" NA      "small" "small" "large" "small" "small" "large" "large"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "large"   "small"   "UNKNOWN" "large"   "large"   "large"   "UNKNOWN" "small"   "small"   "small"  
[11] "small"   "large"   "small"   "small"   "small"   "UNKNOWN" "small"   "small"   "large"   "small"  
[21] "small"   "large"   "large"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1] 623   0  NA 995 646 601  NA   0   0   0   0 862   0   0   0  NA   0   0 544   0   0 723 996

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 1 5 2 4

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  1  5  2  4

duplicated( duplicatedNumbers )

 [1] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 23

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 996

which.min( manyNumbersWithNA )

[1] 18

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 26

range( manyNumbersWithNA, na.rm = TRUE )

[1]  26 996

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 623 456  NA 995 646 601  NA 334 319 102 158 862  85 187 429  NA 375  26 544 177 212 723 996

sort( manyNumbersWithNA )

 [1]  26  85 102 158 177 187 212 319 334 375 429 456 544 601 623 646 723 862 995 996

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  26  85 102 158 177 187 212 319 334 375 429 456 544 601 623 646 723 862 995 996  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 996 995 862 723 646 623 601 544 456 429 375 334 319 212 187 177 158 102  85  26  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 623 456  NA 995 646

order( manyNumbersWithNA[1:5] )

[1] 2 1 5 4 3

rank( manyNumbersWithNA[1:5] )

[1] 2 1 5 4 3

sort( mixedLetters )

 [1] "C" "F" "g" "k" "O" "q" "Q" "u" "U" "w"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 5.0 9.0 3.5 6.5 6.5 2.0 9.0 1.0 3.5 9.0

rank( manyDuplicates, ties.method = "min" )

 [1] 5 8 3 6 6 2 8 1 3 8

rank( manyDuplicates, ties.method = "random" )

 [1]  5 10  4  7  6  2  9  1  3  8

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.0000000 -0.5000000  0.0000000  0.5000000  1.0000000  0.9476064 -0.4420486 -1.9002042  0.1262122
[10]  1.2293367  1.3219107  0.3137758 -0.1094167  0.5678630  0.5842872

round( v, 0 )

 [1] -1  0  0  0  1  1  0 -2  0  1  1  0  0  1  1

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0  0.9 -0.4 -1.9  0.1  1.2  1.3  0.3 -0.1  0.6  0.6

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00  0.95 -0.44 -1.90  0.13  1.23  1.32  0.31 -0.11  0.57  0.58

floor( v )

 [1] -1 -1  0  0  1  0 -1 -2  0  1  1  0 -1  0  0

ceiling( v )

 [1] -1  0  0  1  1  1  0 -1  1  2  2  1  0  1  1

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 x 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

⌂ Contents 📁 Materials ← Vectors Factors →

---

Copyright © 2021 Biomedical Data Sciences (BDS) | LUMC